Helmholtz Imaging Projects
Published on 07.10.2024
BrainShapes
The project explores the 3D structure of the human brain by creating a digital ‘map’ of the brain and examining its unique genetic properties, potentially linking genetic variations to brain disorders.
Published on 19.08.2024
CLARITY
Dose-escalated radiotherapy of lung cancers requires precise monitoring of lesions and nearby organs at risk. Current methods are able to track ultra-central lesions but neglect their deforming vicinity, risking unacceptable toxicity to aortico-pulmonary structures. AI-based anomaly detection and generative AI models can address both requirements in real-time.
Published on 14.08.2024
3DforestSIF
3DforestSIF seeks to correct airborne solar-induced fluorescence (SIF) data from forests for canopy structural and illumination effects, providing valuable insights for the early detection of forest stress.
Published on 09.08.2024
HYPER-AMPLIFAI
The project aims to make advanced AI models accessible for Hyperspectral Earth Observation, reducing computational demands, and improving environmental assessments through user-friendly interfaces.
Published on 28.08.2023
ImageTox
ImageTox wants to establish an automated image-based system to assess zebrafish larval development. This will allow for a fast and unbiased evaluation of pathophysiological events during toxicological studies. To achieve this, the imaging process has to be optimized and a reliable model for sequence recognition based on deep learning has to be developed.
Published on 28.08.2023
HighLine
MR images of roots and vessels are very similar: both display thin, line-like objects. The aim of the project is to increase image quality of both kind of MR data by exploiting their similarity. HighLine aims at obtaining high quality images in reduced scan time to lower patient burden and increase patient and plant throughput by adapting state-of-the-art 3D image enhancement methods, and developing new deep-learning based methods.
Published on 28.08.2023
EMSIG
Microfluidic live-cell imaging (MLCI) unlocks spatio-temporal insights into population heterogeneity emerging from a single cell. EMSIG brings smart live-event detection capabilities to MLCI to facilitate the adaptive optimization of biological event resolution and autonomously counteracting deteriorating image qualities.
Published on 28.08.2023
DIPLO
This project will develop a user-friendly software platform to analyze plankton images independent of the instrument with which images were collected. This will help to compare data and create a common database, which is a critical step towards an image-based ecosystem component of a “Digital Twin Ocean”.
Published on 28.08.2023
Deep4OM
Deep4OM aims to develop a deep learning-based framework for optoacoustic mesoscopy image analysis, enabling quantification of human skin biomarkers for non-invasive skin disease diagnosis. Deep4OM has the potential to change the landscape of non-invasive skin imaging, and could significantly promote the diagnostic and prognostic applications of RSOM in clinical routine.
Published on 28.08.2023
BENIGN
The BENIGN project aims to enable non-invasive molecular imaging with cellular resolution in vivo at depths of several millimeters. This will be achieved using light from the shortwave infrared (SWIR) range (1000-2000 nm), which has less scattering and autofluorescence compared to the visible and near-infrared spectral range. Bright and targeted imaging agents are needed to fully exploit this range. The project will develop a new approach using lanthanide-based core-shell structures that emit light in the 1500-2000 nm range.
Published on 28.08.2023
AutoCoast
Coastal erosion enhanced by climate change has become an increasing global threat, which requires rapid detection and reliable risk assessment. AutoCoast aims to provide advanced and reliable remote sensing-based AI tools to quantify coastline change rate at high-resolution and unravel the linkage between coastline change rate and natural and anthropogenic drivers at regional to global scale.
Published on 28.08.2023
AIOrganoid
AIOrganoid will apply cutting-edge imaging techniques and develop novel AI-based solutions to facilitate human lung organoid formation with high yield and fidelity, bridging the gap between cell biology and computational imaging.
Published on 21.03.2023
WeMonitor
Satellite imagery makes it possible to detect spatio-temporal anomalies on the Earth’s surface, including natural hazards such as landslides, deforestation, or the emergence of large waste dump sites. This project aims to use artificial intelligence to detect these changes at an early stage and to be able to monitor their progress.